With the rapid growth of Artificial Intelligence of Things (AIoT), identity management and trusted communication have become critical for system security and reliability. Continuous AI learning and large-scale device connectivity introduce challenges such as permission drift, cross-domain access, and fine-grained API calls. Traditional identity management often fails to balance privacy protection with efficiency, leading to risks of data leakage and misuse. To address these issues, this paper proposes a blockchain-based self-sovereign identity (SSI) management mechanism for AIoT. By integrating SSI with a zero-trust framework, it achieves decentralized identity storage and continuous verification, effectively preventing unauthorized access and misuse of identity data. The mechanism employs selective disclosure (SD) technology, allowing users to submit only necessary attributes, thereby ensuring user control over self-sovereign identity information and guaranteeing the privacy and integrity of undisclosed attributes. This significantly reduces verification overhead. Additionally, this paper designs a context-aware dynamic permission management that generates minimal permission sets in real time based on device requirements and environmental changes. Combined with the zero-trust principles of continuous verification and least privilege, it enhances secure interactions while maintaining flexibility. Performance experiments demonstrate that, compared with conventional approaches, the proposed zero-trust architecture-based SSI management mechanism better mitigates the risk of sensitive attribute leakage, improves identity verification efficiency under SD, and enhances the responsiveness of dynamic permission management, providing robust support for secure and efficient AIoT operations.

With the rapid development of wireless communication and big data analysis technologies, the storage of massive amounts of data relies on third-party trusted storage, such as cloud storage. However, once data are stored on third-party servers, data owners lose physical control over their data, making it challenging to ensure data integrity and security. To address this issue, researchers have proposed integrity auditing mechanisms that allow for the auditing of data integrity on cloud servers without retrieving all the data. To further enhance the availability of data stored on cloud servers, multiple replicas of the original data are stored on the server. However, in existing multi-replica auditing schemes, there is a problem of server fraud, where the server does not actually store the corresponding data replicas. To tackle this issue, this paper presents a formal definition of authentic replicas along with a security model for the authentic-replica sampling mechanism. Based on time-lock puzzles, identity-based encryption (IBE) mechanisms, and succinct proof techniques, we design an authentic replica auditing mechanism. This mechanism ensures the authenticity of replicas and can resist outsourcing attacks and generation attacks. Additionally, our schemes replace the combination of random numbers and replica correspondence tables with Linear Feedback Shift Registers (LFSRs), optimizing the original client-side generation and uploading of replica parameters from being linearly related to the number of replicas to a constant level. Furthermore, our schemes allow for the public recovery of replica parameters, enabling any third party to verify the replicas through these parameters. As a result, the schemes achieve public verifiability and meet the efficiency requirements for authentic-replica sampling in multi-cloud environments. This makes our scheme more suitable for distributed storage environments. The experiments show that our scheme makes the time for generating copy parameters negligible while also greatly optimizing the time required for replica generation. As the amount of replica data increases, the time spent does not grow linearly. Due to the multi-party aggregation design, the verification time is also optimal. Compared to the latest schemes, the verification time is reduced by approximately 30%.

In today’s rapidly evolving data environment, secure and efficient storage solutions are fundamental to supporting the robust development of the data economy. Trustworthy data space serves as an innovative technological framework for addressing critical challenges in data circulation. It is specifically designed to facilitate the secure exchange in data elements and overcome trust barriers in cross-organizational data sharing. However, current decentralized storage architectures still have significant implementation gaps. Practical deployment and system integration remain substantial challenges for existing technological solutions. To address these issues, this paper first conducts a systematic analysis of existing trusted data storage methods. On this basis, it proposes a data-secure storage mechanism based on polynomial commitment. This mechanism uses polynomial commitment to implement data storage and verification, thereby ensuring data integrity and consistency. Meanwhile, it integrates homomorphic signature technology to guarantee the authenticity of data sources without disclosing original data. Additionally, a data modification recording function is introduced to ensure the traceability of all operations. Experimental results show that the proposed scheme achieves superior performance in three key aspects: communication overhead, storage efficiency, and data update costs.

This study proposes an explainable framework for developmentally informed pediatric rehabilitation and evaluates audited components on three separate public datasets. Pediatric stroke is a prospective application; no pediatric stroke cohort or synchronized multimodal experiment is included. A reproducibility audit replaced previously reported speech results that used non-independent source variants. In a new calibration-free experiment using 600 Speech Commands recordings from 343 speakers, all variants of each source and all recordings from each speaker remained in one partition. On the speaker-disjoint test set, a 12-feature random forest achieved 0.822 accuracy (95% participant-clustered bootstrap interval 0.774–0.867), whereas four calculated acoustic proxies and a four-concept Sequential Concept Bottleneck Model each achieved 0.578 accuracy. Performance generally approached chance when an entire perturbation family was withheld, demonstrating transformation specificity rather than clinical speech validity. The audited motor video experiment was view-specific rather than cross-view; its pooled accuracy was 0.202, below the 0.217 majority-class baseline, with zero recall for five rare classes. A participant-grouped random forest using six pose-derived proxies reached 0.184 accuracy and 0.095 macro-F1. In all 49 available BJCMC transcripts, leave-one-child-out age normalization reduced the Spearman correlation between speech rate and age from 0.328 (95% interval 0.065–0.535) to 0.042 ($-0.052$–0.124). A feature-level attribution diagnostic added repeated random controls and stability analysis. These results support a reproducible component-level proof of concept while identifying substantial requirements for clinical, multimodal, and motor validation.